Flying Without a Net: Space Radiation Cancer Risk Predictions without a Gamma-Ray Basis

Abstract

It is well known that the spatial distribution of ionization in cells and tissue from heavy ions and other high linear energy transfer (LET) radiation leads to qualitative and quantitative differences in biological effects compared to low LET radiation such as gamma-rays. However, models used to estimate risks involve extensive use of gamma-ray data, including low LET radiation epidemiology, the role of gamma-rays in estimates of quality factors (QF), and the dose and dose-rate reduction effectiveness factor (DDREF). In tumor induction studies, high LET radiation typically have demonstrable dose responses in many animal strains and tissue, while gamma-ray exposures often lead to a weak or poorly determined dose response at low to moderate doses (<2 Gy) leading to large uncertainties in QF estimates. Here we consider an alternate risk prediction approach, avoiding low epidemiology, the QF and DDREF, by formulating a fluence based track structure model of excess relative risk (ERR) with parameters estimated from animal studies with heavy ions and neutrons for the induction for lung and breast cancer in females and liver cancer in males. The ERR model is applied directly with cancer rates for the US population to predict lifetime risks to astronauts at solar minimum. Results for male liver and female breast cancer risk show that the ERR model agrees fairly well with estimates of a QF model with estimates of non-targeted effects (NTE), and is about 2-fold higher than the QF model that ignores NTE. The effective damage area derived by the ERR model for breast and liver tumors is several times that of a mammalian cell nucleus, which suggests NTE likely contribute to cancer risk. For female lung cancer risk, the ERR model predicts 2-fold and 5-fold lower risk compared to the QF models with or without NTE, respectively. We suggest that the direct ERR approach when coupled with improved experimental models of tissue specific cancers representing human risks would lead to large reductions in the uncertainties in space radiation risk projections by avoiding low LET uncertainties.